Metaheuristic optimization and computational investigation of resistance spot welding process parameter employing Jaya, TLBO and Rao-II algorithm

Resistance spot welding (RSW) is a widely used metal joining process in the automotive industry. Significant challenges associated with RSW in automotive industries are weld quality, consistency, distortion of workpieces, and electrode wear despite all assets. These problems often arise due to the improper selection of process parameters. Therefore, the present study aims to evaluate the effectiveness of the optimization algorithm in addressing the issue related to RSW. This work applies three metaheuristic optimization techniques, i.e., Jaya, TLBO, and Rao-II algorithms, to optimize the process parameter in RSW. Metaheuristics algorithms are used to solve computationally complex problems. It can also handle problems that are nonlinear, non-convex, discrete, and multiobjective. This paper analyzes three case studies to evaluate the performance of the Jaya, Rao-II, and Teaching learning-based optimization (TLBO) algorithms. The results of these algorithms are compared with a well-established algorithm such as Genetic algorithm, Artificial bee colony, and ANN-GA algorithm. The comparison results are then used to draw conclusions about each algorithm's capabilities and to identify potential improvement areas.